La Dolce Vita: Hedonic Estimates of Quality of Life in Italian Cities∗ Emilio Colombo,† Alessandra Michelangeli,‡ Luca Stanca§ University of Milan - Bicocca December 2010 Abstract This paper provides an assessment of quality of life in Italian cities using the hedonic approach. We analyze micro-level data for housing and labor markets to estimate compensating differentials for local amenities within five domains: climate, environment, services, society and economy. The estimated implicit prices are used to construct overall and domain-specific quality of life indices. We find that differences in amenities are reflected in substantial compensating differentials in housing prices, whereas the effects on wages are relatively small. Quality of life varies substantially across space and is strongly related to differences in public services and economic conditions. Overall, quality of life is highest in medium-sized cities of the Center-North, displaying relatively high scores in all the domains considered. Northern cities fare better with respect to services, social and economic conditions, while relatively worse for climate and environmental conditions. JEL: C4, D5, H4, J3, J6, P2, P3, Q2, R2 Keywords: quality of life, hedonic prices, housing markets. ∗

We gratefully acknowledge the Osservatorio del Mercato Immobiliare for data on housing transactions, and the Fondazione Rodolfo De Benedetti for labor market data. Financial support by the Italian Ministry of University and Research is gratefully acknowledged. We thank participants to conferences in Rotterdam (AREUEA), Firenze, and Aosta (AISRe) for helpful comments. The usual disclaimer applies. † Department of Economics, University of Milan Bicocca. Piazza dell’Ateneo Nuovo 1, 20126 Milan, Italy. E-mail: [email protected] ‡ Law and Economics Department, University of Milan Bicocca, Piazza dell’Ateneo Nuovo, 1, 20126 Milan, Italy, E-mail: [email protected] § Corresponding author: Department of Economics, University of Milan Bicocca, Piazza dell’Ateneo Nuovo 1 (U6-367), 20126 Milan, Italy. E-mail: [email protected]

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1

Introduction

In recent years, as fiscal federalism has come to the forefront of the policy debate in several countries, the comparison of quality of life (QoL) across regions and metropolitan areas has become a key issue for policymakers and the general public. As a consequence, the assessment of living conditions and their determinants has received increasing attention, well beyond the academic debate (Rappaport, 2009). A large body of literature has developed, proposing alternative methods for measuring quality of life in regions and cities on the basis of their observable characteristics (see e.g. Blomquist, 2007, Lambiri et al., 2006, for recent reviews).1 Within this literature, quality of life is generally defined as the weighted average of a set of local amenities. One of the key issues is therefore how to appropriately weight the different amenities. Following the theoretical approach proposed by Rosen (1979) and extended by Roback (1982), several variants of the hedonic price method have been used to value amenities and construct quality of life indicators. Within this framework, households’ location decisions reveal their preferences for the bundle of attributes that characterize urban areas. The economic value of a local amenity can be determined on the basis of the housing prices households are willing to pay and the wages they are willing to accept to locate in a given area. The basic intuition is that, in a spatial equilibrium, households are willing to pay higher rents, or accept lower wages, to live in areas with better amenities. Quality of life can therefore be measured, and compared across areas, by weighting local amenities with the implicit prices derived from compensating differentials in housing and labor markets. Differences in the quality of life index thus obtained represent the premium that households are willing to pay to live in an area with a given set of amenities.2 Over the last decades, several studies have followed this approach, differing in terms of scope, selection of amenities, and spatial disaggregation level. While the seminal contributions to this literature focus on wage differentials, several more recent studies consider either rent differentials (e.g. Cheshire and Sheppard 1995, Giannias, 1998, Shultz and King 2001), or both wage and rent differentials (Roback, 1982, Kahn, 1995, Berger et al., 2003). A number of recent studies link the analysis of quality of life to other fields, such as urban competitiveness and growth (Deller et al., 2001, Monchuk et al., 2007, Wu and Gopinath, 2008), migration (Douglas and Wall, 2000), 1

See also Luger (1996), Diener and Suh (1997) and Gyourko et al. (1999) for earlier reviews of alternative approaches to the measurement of quality of life. 2 The Rosen-Roback framework of compensating differentials has been modified to include agglomeration effects (Blomquist et al., 1988), taxation effects (Gyourko and Tracy, 1989, 1991) and distance.

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and environmental quality (Brasington and Hite, 2005, Redfearn, 2009).3 While several applications of the hedonic approach to the measurement of quality of life across urban areas exist for the United States (e.g. Blomquist et al., 1988, Kahn 1995, Costa and Kahn 2003, Ezzet-Lofstrom, 2004, Shapiro 2006, Rappaport, 2008, 2009, Winters, 2010), there are relatively fewer studies comparing quality of life across cities outside the US (e.g. Giannias, 1998, Berger et al., 2003, Srinivasan and Stewart, 2004, Buettner and Ebertz, 2009). The present study is, to the best of our knowledge, the first application of the hedonic approach to micro-level housing and labor market data to measure and compare quality of life across Italian cities.4 We use individual-level data for wages and housing prices, together with city-level data on local amenities to estimate compensating differentials in labor and housing markets. We obtain implicit prices for amenities within five main domains: climate, environment, services, society and economy. The estimated implicit prices are then used to rank the 103 Italian province capitals on the basis of overall and domain-specific quality of life. Our analysis addresses two main questions. First, what are the main determinants of quality of life in Italy? More specifically, what is the value that Italians attribute to, say, climate and environment, as opposed to public services and socio-economic conditions as determinants of their quality of life? Second, how is overall and domain-specific quality of life distributed across Italian cities? The results indicate that the presence of amenities results in large compensating differentials for the housing market, whereas the effects on wage differentials are relatively small, reflecting the relative rigidity of wages and low regional mobility in the Italian labor market. We find substantial geographical variation in quality of life, with the overall index reflecting different classes of amenities across cities. Quality of life is highest in medium-sized towns of the Center-North. Northern cities generally fare better for services and economic conditions, while relatively worse for climate and environmental conditions. The opposite pattern applies to cities located in the South. The domain-specific indicators are related to the overall index in various degrees. Climatic and environmental conditions are negatively related to 3

See also Morawetz et al. (1977), Alesina et al. (2001) and Oswald and Wu (2010) for studies linking quality of life and individual well-being. 4 QoL indicators have been developed in the Italian context using different methodologies. Maddison and Bignamo (2003) estimate the marginal willingness to pay for climate variables in Italian cities. Schifini D’Andrea (1998) relies on socio-economic indicators to assess quality of life in Italy in a comparative perspective. Cicerchia (1996) proposes a set of objective and subjective indicators of quality of life based on land supply and demand, territorial loading, equilibrium and spill-over of urban systems, and critical population mass. Nuvolati (2003) analyses the evolution of QoL in the 103 Italian provinces from 1989 to 2001 following the approach proposed by Bagnasco (1977), who studies the links between socioeconomic development and living conditions in the Italian regions.

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overall QoL, while social conditions are positively but weakly related to QoL. Public services and economic conditions are positively and strongly related to overall quality of life. The remainder of the paper is structured as follows. Section 2 briefly reviews the theoretical framework. Section 3 describes the data. Section 4 discusses the methodology. Section 5 presents the results. Section 6 concludes. Details on the data sets and definition of variables used for the empirical analysis are provided in the Data Appendix.

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Theoretical Framework

Following Rosen (1979) and Roback (1982), consider a spatial equilibrium model where households and firms compete to locate in areas characterised by different bundles of amenities. Households derive utility from consuming a composite consumption good, housing and local amenities. Access to local amenities is obtained by living in a given location. Labour income allows the purchase of both the composite consumption good and housing. In city j, a household’s indirect utility is: v j = v(wj − rj , Aj )

(1)

where v(·) is the maximum level of utility that the household can obtain with wage w, housing rent r, and the vector of amenities Aj , with ∂v/∂w > 0, ∂v/∂r < 0 and ∂v/∂aij ≷ 0 depending on whether ai is a consumption amenity or disamenity. The price of the composite consumption good (x) is normalised to 1, so that xj = wj − rj . The composite consumption good is produced by firms that use a constant returns to scale technology using labour and land as inputs. The consumption good is tradable and its price is fixed by international competition. The unit production cost in city j is: cj = c(wj , rj ,Aj )

(2)

with ∂c/∂w > 0, ∂v/∂r > 0 and ∂v/∂aij ≷ 0 depending on whether ai is a production amenity or disamenity. Equilibrium requires the absence of spatial arbitrage, so that household utility and production costs are equal across cities: u∗ = v(wj − rj , Aj )

(3)

1 = c(wj , rj , Aj )

(4)

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In a spatial equilibrium, differences in wages and housing prices should compensate individuals and firms for differences in location-specific characteristics.5 Figure 2 illustrates the equilibrium determined by equations (3) and (4). Better amenities cause the iso-utility curve to shift up, resulting in higher housing costs and lower wages, under the assumption that amenities do not have productivity effects. If, however, local amenities also affect firms’ productivity, the net effect on wages is ambiguous. A higher level of a production amenity would result in an upward shift of the iso-cost curve. While there is no ambiguity in the effect on rents, there can be an increase in equilibrium wages if the effect on firms’ labour demand dominates the effect on households’ labour supply. r6 e

e

e

... ... .... . . . ... ... .... .... . . . .... ... .... ..... . . . ..... .... ...... ...... . . . . . . .. ...... ....... ....... ........ . . . . . . ........ ......... ......... .......... . . . . . . . . . . . ............. ................ ...................

e

e

e

e

e

e

e

e

e

e

e

e

e

e e

u∗ = v(wj − rj ; Aj )

1 = c(wj ; rj ; Aj ) -

w

Figure 1: Spatial equilibrium with rents and wages Wages and housing costs can be used to obtain implicit prices for amenities. Taking the total differential of (3), and rearranging, we obtain: fi =

∂V ∂V drj dwj / = − ∂aij ∂xj daij daij

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(5)

Rosen (1979) points out how this approach is related to the theory of local public goods (Tiebout, 1956, and Stigler, 1957): The observed combinations of urban amenities, wage rates and costs of living among different cities satisfy an equilibrium condition reminiscent of a “voting with your feet” criterion; each household’s locational choice maximises its welfare and no family can be made better off by moving to another city. (Rosen 1979, p.74).

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where drj /daij is the equilibrium compensating differential for housing costs and dwj /daij is the equilibrium wage compensating differential. The marginal valuation of an amenity can therefore be obtained from the marginal responses of housing costs and wages. Given the estimates of the implicit prices fi , an index of quality of life for city j can be constructed as the weighted sum of each amenity i, with weights given by the implicit prices fi that reflect households’ preferences. X QoLj = fi aij (6) i

Urban QoL indices thus constructed can be interpreted as the monetary value that the representative household attributes to the bundle of amenities available in each city.

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Data

The empirical analysis relies on three different data sets covering a period between 2001 and 2009. Two data sets provide individual-level information on the housing market and the labor market, respectively. The third data set provides city-level information on amenities. A detailed description of the variables and sources is provided in the Data Appendix. We focus on cities defined as the municipalities of province capitals. The unit of analysis is therefore the municipal area of province capitals, rather than the whole provincial territory.6 Housing market data are from the Real Estate Observatory of the Agenzia del Territorio (AT), and refer to individual house transactions in Italian cities (province capitals) between 2004 and 2009 at semi-annual frequency.7 In addition to house sale prices, the data set provides a detailed description of structural and neighbourhood characteristics, such as surface area, age, number of bedrooms and bathrooms, floor level, number of garages or car parks, location (center, semi-center, suburb), quality of building (good, average, bad) quality of the area, and distance from transport system. Table 11 in the Data Appendix provides a detailed description of housing characteristics, while Table 12 reports average housing prices at 2004 constant prices by city. Labour market data are from the Italian National Social Security Institute (Inps) at annual frequency between 2001 and 2002, and refer to individual workers in the private sector. The data set provides information 6

This definition should be considered when interpreting city rankings and geographical representations, as in Figure 2. 7 The present study focuses only on sales, while excluding the rental market. It should be observed that 70.2% of Italian households own their house (Istat, 2008).

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on annual earnings, type of occupation, full time or part-time work status, contract length, province of work. The employee’s longitudinal records are linked to the demographic and firms archives in order to have information on worker characteristics (gender, age, nationality, province of residence, etc.) and firm characteristics (size and sector of activity). We restrict the sample to all employees aged between 16 and 75, who live in the same city where they work for at least 30 weeks in a year.8 Seasonal workers are not included in the sample.9 Annual earnings are total yearly wages net of social contributions paid by firms, but gross of social contributions and income taxes paid by workers. Table 13 in the Data Appendix reports descriptive statistics for worker and firm characteristics, while Table 14 displays average wages at 2004 constant prices by city. Information on local amenities and characteristics for the municipalities of the 103 Italian provinces has been collected for the period 2001-2008 from Istat and other sources (see table 10 in the Data Appendix for details). We consider 15 city-level amenities, that fall within five different domains: climate, environment, services, society and economy. Climate is proxied by three indicators: temperature (yearly average), precipitation (monthly average), humidity (yearly average). The environmental domain is based on both physical features of the territory (percentage of green areas of the city and a dummy variable indicating a coastal city) and pollution (number of polluting agents present in the air). Indicators for the quality of services focus on education (teacher-pupil ratio), culture (index of cultural infrastructure, measuring several dimensions of the city’s cultural offerings, such as museums, cinemas, theaters, etc.), and transport (multi-modal indicator that considers accessibility by air, train and car). The society domain refers to the characteristics of those who live in the city: we include a measure of violent crime, human capital (tertiary education), civicness of the population (voters’ turnout in local elections), and the share of foreigners in total population. Economic conditions are measured by value added per head and the unemployment rate. Summary statistics for the amenities are provided in Table 1. 8 Almost all workers (from the 5th to the 95th percentile) are between 22 and 55 years old. However, we consider younger and older people still at work to account for different preferences for amenities. 9 Wages of part-time workers have been converted to full-time equivalent using a 1.4 multiplicative factor. This conversion is based on the average number of hours worked in a part-time job that generally range between 4 and 6 (about two thirds of the daily total number of hours worked for a full-time job).

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Table 1: Local amenities, 2001-2008 Variable Mean Std. Dev. Min. Max. Precipitation (mm per month) 68.6 22.4 28.9 139.7 Temperature (degrees, average) 15.7 1.8 12.8 19.9 Humidity (per cent) 72 4 57.3 79.9 Coast (dummy) 0.3 0.5 0 1 Green areas (per cent) 6.9 11.2 0.1 71.9 Air Pollution (number of agents) 7.7 2.6 1.3 15.4 Education (TPR, per cent) 9.9 1.6 8.3 22.6 Transport (accessibility index) 91.6 24 47 161 Cultural Infrastructure (index) 87.3 77.6 18.9 579.2 Violent Crime (per 1000) 4.1 1.5 1.1 9.9 Civicness (voting turnout, per cent) 75.4 5 50.1 84.8 University Enrollment (per cent) 5.4 8.3 0 40 Foreigners (per cent) 6.3 3.7 0.4 15.4 Value Added per Head (th. euros) 17.6 3 12.1 24 Unemployment Rate (per cent) 11.1 7.5 2.8 31.4 See the Data Appendix for details on sources and definitions of variables.

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Methods

We measure the implicit price of amenities by estimating two separate equations for housing prices and wages: phjt = β0 + β1 Xht + β2 Ajt + εhjt

(7)

wzjt = γ0 + γ1 Zzt + γ2 Ajt + ηzjt

(8)

where phjt is the annual expenditure for housing unit h in city j at time t, Xhj is a vector of housing characteristics, Ajt is a vector of amenities, wzjt is the wage of individual z in city j at time t, ¡ Zzj2 ¢is a vector of individual 2 characteristics, εhjt v N (0, σε ) and ηzjt v N 0, ση . The application of the hedonic approach is based on the assumption that there are no unobserved characteristics for housing units, workers and cities, that are correlated with observable local amenities. The detailed information on housing and individual characteristics (Xhjt and Zijt ) is used to control for the heterogeneity of houses and workers. Structural characteristics in Xhjt include flat size, age of building, number of bedrooms and bathrooms, floor level, number of floors, number of lifts, number of garages or car parks, housing type, unit conditions, housing features, value type and location, quality of building. Neighbourhood characteristics include quality of the area, distance from transport system, distance from public services and commercial services. Worker and firm characteristics in Zijt include gender, 8

age, nationality, province of residence, type of occupation, contract length, size of the firm and sector of activity. We control for cities’ unobserved heterogeneity by including indicators for urban density and population size, a proxy for economic structure (the share of services in total value added) and a dummy for region capitals. Year dummies are also included to account for time fixed effects.10 Nominal values for both housing prices and wages are converted to 2004 constant prices. Equations (7) and (8) are estimated by OLS using approximately 128,000 and 158,000 observations, respectively. Robust standard errors are used with clustering at city-level. In order to obtain the full price of each amenity b and γ the estimated coefficients β b2 in (7) and (8) must be converted into 2 annual household expenditures. Estimated coefficients for the housing price equation are converted into imputed annual rents applying a 7.85 per cent discount rate, as in Blomquist et al. (1988). The estimated coefficients for the wage equation are multiplied by 1.64, the average number of workers per household (Bank of Italy, 2008), in order to obtain household wages comparable to housing expenditures. This allows the computation of the full price fi for each amenity. As in equation (6) they are multiplied by the value of each amenity in each city j, relative to the overall mean, obtaining a value of the quality of life index. Finally, we rank the 103 Italian provinces according to the overall index. In addition to the overall index, we also obtain QoL sub-indices and rankings for individual domains (climate, environment, services, society, economy) and the respective contribution of each sub-index to the overall index.

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Results

This section presents the results of the empirical analysis. We start by discussing the implicit prices estimated from housing price and wage equations. We then present the overall quality of life index for the 103 province capitals. Finally, we consider quality of life rankings for individual domains and their contributions to the overall index.

5.1

Implicit prices

Table 2 reports estimation results for equations (7) and (8). For both equations, we consider two alternative specifications with the dependent variable expressed either in levels or logarithms. As the results for the two specifications are in all cases qualitatively similar, for brevity and ease of inter10

Gyourko and Tracy (1991) also include local taxes in the set of amenities locally produced. We neglect this component since the Italian fiscal system leaves very limited room for local authorities in affecting the tax system.

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pretation in the following we focus on the results for the specification in levels. In the housing price equation (columns 1-2), the coefficients for all the 15 amenities have the expected sign and jointly statistically signficant. Controlling for structural and neighborhood characteristics, housing prices are higher in cities with higher temperature, lower humidity and lower precipitations. Housing prices are also higher in cities with less pollution, more green areas, located on the coast. Focusing on services, positive differentials are observed in cities with higher teacher-pupil ratio, better transports and better cultural infrastructure. Regarding social conditions, housing prices are lower in cities with higher crime rates and shares of foreigners, while they are positively related to civicness and university enrollment. Economic conditions are associated to substantial differentials: housing prices are significantly higher in cities with higher value added per head and lower unemployment rate (-3866 euros for one additional percentage point). Although standard errors are relatively large, so that only 6 amenities are individually statistically significant, amenities are jointly significant for each of the five domains considered. The coefficients for the amenities in the wage equation (columns 3-4), instead, in many cases do not have the expected sign and are generally not statistically significant.11 For most amenities, the sign of the estimated coefficient in the wage equation is the same as for the housing equation. This may indicate that the local amenities may be affecting not only households, but also firms, so that the net effect on wages is ambiguous. For example, to the extent that crime is a disamenity for both households and firms, higher rates of violent crime in a given city will result in both lower labor supply by households and lower labor demand by firms. An Alternative interpretation lies in the well known rigidities of the Italian labor market. Wage rigidity and low labor mobility imply that wages may not adjust to compensate for different amenities across cities. Our data set refers to wages for dependent employment, regulated by sectoral nation-wide contracts that impose strong limitations to regional wage differences for a given occupation. The relatively low interregional mobility of labor in Italy is also well documented in several studies (see e.g. Cannari et al., 2000, and Eurofound, 2006).12 Table 3 presents the implicit prices of amenities derived from the esti11

Similar results for the effect of amenities on household income are obtained in Buettner and Ebertz (2009). 12 The choice of including only dependent workers in our sample, while excluding selfemployed workers, was made to obtain higher reliability of statistical information concerning declared wages. The empirical evidence indicates a low tax evasion rate for dependent workers that is instead much higher for the self-employed (see, for example, Bordignon and Zanardi, 1997, and Marino and Zizza, 2008). As a consequence, the wage equation would not be informative for the latter category of workers.

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Table 2: Estimated compensating differentials, housing and wage equations Housing equation Level Log Precipitation -155.56 -0.09 (-0.46) (-0.63) Temperature 6287.14 2.38 (1.20) (0.99) Humidity -1531.11 -0.90 (-1.68) (-2.07) Coast dummy 25562.69 12.68 (1.71) (1.81) Green areas 421.64 0.30 (1.11) (1.63) Air Pollution -2508.11 -1.22 (-1.96) (-2.03) Education (Teacher-Pupil Ratio) 1777.39 -0.33 (0.24) (-0.09) Transport 592.89 0.32 (1.79) (2.18) Cultural Infrastructure 78.18 0.03 (1.05) (0.84) Violent Crime -6798.83 -2.74 (-2.32) (-1.96) Civicness 1586.79 0.65 (1.54) (1.39) University Enrollment 505.47 0.13 (0.54) (0.35) Foreigners -466.51 -0.53 (-0.22) (-0.53) Value Added per Head 2800.63 1.20 (1.79) (1.72) Unemployment Rate -3866.34 -2.04 (-2.49) (-2.93) 2 R 0.63 0.72 Number of observations 128355 128355

Wage equation Level Log -5.08 -0.02 (-1.66) (-1.07) 33.57 0.08 (0.60) (0.27) -38.94 -0.19 (-3.62) (-3.33) 87.54 0.32 (0.61) (0.42) 0.14 0.01 (0.03) (0.30) 19.53 0.13 (0.93) (1.17) 357.94 1.92 (4.13) (4.03) 10.09 0.06 (2.85) (3.03) -0.29 -0.00 (-0.57) (-0.15) -19.25 -0.11 (-0.65) (-0.68) 6.87 0.01 (0.56) (0.16) 18.83 0.08 (1.64) (1.36) 116.82 0.54 (2.62) (2.25) 78.72 0.28 (1.77) (1.17) -31.43 -0.16 (-1.59) (-1.64) 0.53 0.60 158066 158066

Note: Dependent variable: house prices (columns 1-2) and wages (column 3-4). OLS estimates, t-statistics reported in brackets (heteroskedasticity-robust standard errors, with clustering at city-level). The set of regressors at city-level also includes population size, urban density, share of service sector and a regional capital dummy variable. The housing and wage equations also include structural and neighbourhood characteristics and firm-worker characteristics, respectively, as described in Section 4.

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mates for the linear specifications in Table 2. As illustrated in Section 4, the estimated coefficients for the housing price equation are converted into imputed annual rents using a 7.85 per cent discount rate, while those of the wage equation are multiplied by 1.64, the average number of workers per household. The resulting figures provide the compensating differentials, expressed in euros at constant 2004 prices, of a one-unit change in the corresponding amenity. For example, implicit prices from the housing price equation (column 1) indicate that households are willing to pay 493.5 Euros per year to for additional degree of temperature. Since the implicit price from the wage equation (column 2) is also positive (53.7), the full implicit price (column 3) is 493.5-53.7=439.8 euros. The comparison between columns 1 and 2 indicates that the implicit prices from the housing equation are generally larger than those from the wage equation, so that the full implicit price has always the expected sign, with the only exception of the teacher pupil ratio. Table 3: Estimated implicit prices of amenities Implicit prices Standardized Precipitation Temperature Humidity Coast Green Areas Pollution Education Transport Cultural Infr. Crime Turnout University Foreigners Value Added Unemployment

Housing

Wage

-12.2 493.5 -120.2 2006.7 33.1 -196.9 139.5 46.5 6.1 -533.7 124.6 39.7 -36.6 219.8 -303.5

-8.1 -4.1 -91.2 53.7 439.8 780 -62.3 -57.9 -231.7 140.1 1866.6 888.4 0.2 32.9 368.6 31.2 -228.1 -596.8 572.7 -433.2 -392.7 16.1 30.4 730.8 -0.5 6.6 511.8 -30.8 -502.9 -730.9 11 113.6 568 30.1 9.5 79.1 186.9 -223.5 -817 126 93.9 279.7 -50.3 -253.2 -1892.8

Total

Total

Share

Housing

Total

Housing

-273.1 875.3 -481.1 955.1 371.1 -515 126.5 1118.9 475.9 -775.7 623 329.1 -133.9 654.9 -2268.7

1.0 8.7 2.6 9.9 4.1 6.7 4.4 8.2 5.7 8.2 6.3 0.9 9.1 3.1 21.1

2.7 8.8 4.8 9.6 3.7 5.2 1.3 11.2 4.8 7.8 6.2 3.3 1.3 6.6 22.7

Note: columns 1-3 report the compensating differentials, expressed in euros at constant 2004 prices, of a one unit change in the corresponding amenity. Columns 4 and 5 report the change in QoL associated to a one-standard deviation in the corresponding amenity. Columns 6 and 7 report the relative contribution of each variable to the determination of the overall QoL index. See Section 5.1 for details.

In order to compare the relative size of the effects of different amenities, Table 3 also reports, in columns 4 and 5, the change in QoL associated to a one-standard deviation in the corresponding amenity, using full and 12

housing-only implicit prices, respectively. The results for the housing equation indicate that, among disamenities, unemployment has the largest effect on QoL, followed by violent crime and air pollution. Among amenities, transport, coastal location and temperature have the largest effects on quality of life. The last two columns of Table 3 report the relative contribution of each variable to the overall QoL index.13 This also allows us to assess the relative importance of different groups of amenities. For example, climate and environmental variables account for 16.33% of the overall QoL index and 18.33% of the housing-only QoL index.

5.2

City Rankings

The estimated implicit prices in Table 3, multiplied by the average values for the corresponding amenity in each city, provide quality of life indices at city-level. Tables 4 and 5 report the QoL indices based on full implicit prices and housing equation only, respectively. These overall QoL indices are normalized with respect to the country average, so that they can be interpreted as the amount, in 2004 euros, that households would be willing to pay to live in a city with a given bundle of amenities, relative to a city with the average set of amenities. A comparison of the two indicators indicates that the rankings are very similar. Therefore, given the ambiguities of the implicit prices obtained from the wage equation, in the following we will focus mainly on the results based on the housing equation. The results indicate that amenities account for substantial variation in quality of life. In Table 5, the city with the highest quality of life is Pisa, with a score of 6,502. This indicates that, on average, Italians are willing to pay 6,502 euros for living in a city with a corresponding bundle of amenities, relative to a city with average levels of amenities. This is a considerable compensating differential, when compared with the average annual real wage of approximately 20,000 Euros in our sample. Negative values reflect the price individuals are willing to pay for not living in a given city. At the bottom of our ranking is Enna, with an overall quality of life index of -8,349. This indicates that households would be willing to give up approximately 40% of their average annual wage for not living in a city with a corresponding bundle of amenities. Overall, quality of life is highest in the Center-North, in large (e.g. Bologna, Firenze, Venezia) or medium-sized cities (e.g. Pisa, Trieste, Im13

The relative contribution is constructed with respect to the sum of the absolute values of figures in columns 4 and 5. For example, summing the absolute values of figures in column 5 we obtain 9977.3: this is to be interpreted as the absolute value of the change in QoL associated to a one-standard deviation in every amenity. The weight of each component is therefore calculated with respect to that value. For example, Temperature has a relative contribution of 8.77% = 875.3/9977.3.

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N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Table 4: QoL City Val. Pisa 5070 Trieste 4583 Ancona 3989 Bologna 3868 Firenze 3775 Pesaro 3454 Venezia 3343 Ferrara 2972 Imperia 2857 Siena 2809 Massa 2804 Lodi 2763 Lecco 2643 Livorno 2429 Pavia 2277 Bergamo 2253 Forli 2113 Grosseto 2091 Parma 2083 Reggio E. 2004 Vicenza 1936 Cremona 1785 Chieti 1669 Lucca 1624 Varese 1556 Padova 1511 Gorizia 1465 Como 1409 Latina 1267 Macerata 1256 Ravenna 1254 Mantova 1157 Salerno 1080 La Spezia 1059 Lecce 1047

overall index, full implicit prices, by city N City Val. N City 36 Pistoia 1006 71 Rieti 37 Biella 1001 72 Perugia 38 Treviso 916 73 Aosta 39 Sassari 802 74 Bolzano 40 Sondrio 795 75 Trento 41 Piacenza 741 76 Alessandria 42 Prato 718 77 Vercelli 43 Rovigo 528 78 Matera 44 Cuneo 511 79 Messina 45 Belluno 500 80 Catanzaro 46 Brescia 455 81 Avellino 47 Bari 437 82 Benevento 48 Brindisi 420 83 Napoli 49 Arezzo 417 84 Verbania 50 Verona 279 85 Asti 51 Oristano 258 86 Terni 52 Genova 168 87 Reggio C. 53 Teramo 126 88 Trapani 54 Viterbo 120 89 Nuoro 55 Modena 68 90 Palermo 56 L’Aquila 68 91 Siracusa 57 Cagliari -37 92 Isernia 58 Pescara -122 93 Catania 59 Udine -128 94 Agrigento 60 Pordenone -158 95 Torino 61 Savona -204 96 Cosenza 62 Ascoli P. -240 97 Vibo V. 63 Caserta -275 98 Campobasso 64 Taranto -346 99 Foggia 65 Milano -360 100 Crotone 66 Frosinone -390 101 Potenza 67 Ragusa -588 102 Caltanissetta 68 Rimini -757 103 Enna 69 Roma -973 70 Novara -1120

Note: Source: Istat, Inps and Agenzia del Territorio.

14

Val. -1148 -1180 -1288 -1349 -1390 -1401 -1416 -1618 -1693 -1749 -1767 -1794 -1847 -1966 -2056 -2111 -2437 -2470 -2481 -2506 -2737 -2927 -2998 -3076 -3402 -3415 -3449 -3751 -3948 -4023 -4331 -4963 -7206

N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Table 5: QoL overall index, housing Province Val. N Province Val. Pisa 6502 36 Cuneo 1389 Trieste 6091 37 Ravenna 1372 Bologna 5860 38 Genova 1365 Firenze 5053 39 Macerata 1343 Imperia 4772 40 Modena 1341 Venezia 4636 41 Brescia 1309 Ancona 4402 42 Piacenza 1246 Siena 3905 43 La Spezia 1062 Pesaro 3750 44 Verona 1027 Parma 3578 45 Udine 944 Lodi 3558 46 Arezzo 836 Ferrara 3508 47 Sondrio 816 Reggio E. 3443 48 Pordenone 671 Pavia 3304 49 Rovigo 587 Bergamo 3146 50 Latina 479 Lecco 3116 51 Chieti 371 Forli 2822 52 L’Aquila 228 Livorno 2758 53 Roma 225 Massa 2276 54 Lecce 120 Vicenza 2216 55 Salerno 79 Padova 2211 56 Cagliari 73 Gorizia 2200 57 Belluno 19 Milano 2158 58 Savona 18 Grosseto 2154 59 Viterbo -143 Varese 2079 60 Vercelli -292 Treviso 1970 61 Sassari -463 Cremona 1896 62 Bari -529 Como 1889 63 Novara -626 Mantova 1817 64 Pescara -665 Lucca 1815 65 Aosta -707 Pistoia 1807 66 Oristano -879 Prato 1716 67 Alessandria -1072 Bolzano 1553 68 Teramo -1080 Trento 1534 69 Caserta -1095 Biella 1492 70 Perugia -1105

Note: Source: Istat, Inps and Agenzia del Territorio.

15

only, N 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103

by city Province Brindisi Rieti Rimini Frosinone Ragusa Ascoli P. Asti Verbania Messina Taranto Torino Avellino Catanzaro Benevento Terni Reggio C. Matera Nuoro Isernia Napoli Cosenza Siracusa Vibo V. Trapani Catania Palermo Campobasso Agrigento Potenza Crotone Foggia Caltanissetta Enna

Val. -1326 -1496 -1513 -1692 -1829 -1881 -1892 -1943 -2034 -2474 -2535 -2536 -2587 -2600 -2687 -2814 -2909 -3279 -3711 -3770 -3998 -4107 -4236 -4331 -4334 -4356 -4714 -5127 -5398 -5827 -6030 -6936 -8349

peria, Ancona, Siena, Pesaro, Parma). The largest cities display average scores, with Milan and Rome ranking 23 and 53, respectively. Cities in the South generally display low ranks, with 6 out of 10 of the last cities in the ranking belonging to Sicily.

5.3

Quality of Life by Domain

The indicators presented in Tables 4 and 5 are constructed using all the 15 amenities included in the analysis. We now turn to domain-specific indicators. Figure 2 reports the geographical distribution of the overall and domain-specific quality of life indicators, based on the housing equation implicit prices. Cities in the North generally fare better with respect to services and economic conditions, while relatively worse with respect to climatic and environmental conditions. The opposite applies to the South, while cities located in the center-North are generally characterized by relatively high scores in all the domains considered. Figure 2: QoL Indices, housing only, by domain Overall

(1882,6555] (343,1882] (−1936,343] [−8668,−1936]

Services

(7453,10318] (6319,7453] (5581,6319] [3989,5581]

Weather

(−379,1415] (−1440,−379] (−2528,−1440] [−3710,−2528]

Environment

(407,2747] (−988,407] (−1522,−988] [−2291,−1522]

Society

(8599,10032] (8159,8599] (7151,8159] [4724,7151]

Economy

(2649,3752] (1585,2649] (−2088,1585] [−6602,−2088]

Table 6 displays pairwise correlations between overall and domain-specific quality of life indices. Climatic and environmental conditions are positively related. Similarly, services and economic conditions are strongly positively related. However, climatic and environmental conditions are negatively related to economic and social conditions. As a result, the domain-specific indicators are related to the overall index in various degrees. The climate and environment indices are negatively related to overall QoL, while the 16

society index is positively but weakly related to quality of life. The index for services and, to a larger extent, the index for economic conditions are strongly related to overall quality of life. Table 6: Domain QoL indices, pairwise correlations Bundle of amenities Weather Environment Services Society Economy Environment 0.48 Services -0.43 -0.30 Society -0.15 -0.12 -0.12 Economy -0.69 -0.50 0.58 0.13 Overall -0.30 -0.07 0.66 0.32 0.78 Note: Source: Istat, Inps and Agenzia del Territorio.

Tables 7-9 display the corresponding city rankings by individual domain. The results indicate that the overall quality of life index reflects different classes of amenities in different cities. Overall, the highest ranked cities are characterised by a rather even distribution of amenities, as they score well on almost all of them and the relative importance of different amenities is balanced. These rankings also help to illustrate which factors contribute to an individual city’s ranking. For example, Table 7 indicates that Pisa and Trieste, the first and second top-ranked cities, have high ranks in all of the QoL domains considered. Bologna, the third top-ranked city, is among the top 10 cities for Services, Society and the Economy, but has a relatively low ranking for environmental quality.

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Table 7: QoL ranks by amenity bundle, housing only (1-35) City Overall Weather Environment Services Society Economy Pisa 1 48 9 8 29 57 Trieste 2 31 31 14 31 41 Bologna 3 55 97 6 9 2 Firenze 4 40 71 1 96 24 Imperia 5 2 14 44 93 49 Venezia 6 75 48 4 37 20 Ancona 7 65 12 31 30 39 Siena 8 41 39 56 45 4 Pesaro 9 63 7 53 52 14 Parma 10 25 73 30 79 5 Lodi 11 87 47 9 41 22 Ferrara 12 51 92 35 1 33 Reggio E. 13 26 95 36 27 17 Pavia 14 86 79 5 44 27 Bergamo 15 78 57 17 49 16 Lecco 16 79 51 20 32 7 Forli 17 10 68 40 67 11 Livorno 18 32 28 29 53 64 Massa 19 52 1 58 69 69 Vicenza 20 81 45 34 33 21 Padova 21 74 85 15 55 34 Gorizia 22 67 89 10 64 38 Milano 23 88 77 3 101 9 Grosseto 24 33 11 89 40 52 Varese 25 100 46 18 50 37 Treviso 26 61 102 24 46 18 Cremona 27 99 74 22 23 12 Como 28 84 86 12 65 26 Mantova 29 82 82 27 48 8 Lucca 30 49 50 13 88 43 Pistoia 31 39 37 32 98 25 Prato 32 42 61 23 95 40 Bolzano 33 60 99 97 47 1 Trento 34 59 94 98 38 6 Biella 35 101 41 43 28 32 Note: Source: Istat, Inps and Agenzia del Territorio.

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Table 8: QoL ranks by amenity bundle, housing only (36-70) City Overall Weather Environment Services Society Economy Cuneo 36 44 75 70 36 23 Ravenna 37 73 25 37 90 30 Genova 38 24 56 11 99 56 Macerata 39 64 44 48 35 42 Modena 40 54 101 16 94 10 Brescia 41 85 38 28 92 28 Piacenza 42 98 62 26 63 15 La Spezia 43 53 16 57 89 58 Verona 44 83 98 21 68 31 Udine 45 77 70 67 62 29 Arezzo 46 62 59 66 43 36 Sondrio 47 90 66 59 10 35 Pordenone 48 96 69 62 60 13 Rovigo 49 69 60 61 24 45 Latina 50 34 30 50 25 70 Chieti 51 45 2 72 58 68 L’Aquila 52 70 10 88 39 65 Roma 53 35 88 2 103 60 Lecce 54 23 18 71 4 83 Salerno 55 27 13 60 13 84 Cagliari 56 16 20 75 42 79 Belluno 57 97 43 68 72 3 Savona 58 71 34 41 91 55 Viterbo 59 50 65 45 11 66 Vercelli 60 91 103 42 57 44 Sassari 61 13 6 73 18 87 Bari 62 19 35 38 20 82 Novara 63 102 91 25 82 47 Pescara 64 47 33 65 71 67 Aosta 65 95 96 55 70 19 Oristano 66 11 8 101 15 77 Alessandria 67 94 87 46 77 46 Teramo 68 46 36 81 34 61 Caserta 69 37 52 51 6 81 Perugia 70 68 90 63 66 50 Note: Source: Istat, Inps and Agenzia del Territorio.

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Table 9: QoL ranks by amenity bundle, housing only (71-103) City Overall Weather Environment Services Society Economy Brindisi 71 18 24 49 3 95 Rieti 72 80 81 79 8 63 Rimini 73 72 26 33 100 48 Frosinone 74 58 49 69 5 72 Ragusa 75 4 84 84 12 71 Ascoli P. 76 66 58 76 16 59 Asti 77 92 100 39 81 51 Verbania 78 103 83 54 78 54 Messina 79 3 19 83 61 98 Taranto 80 14 29 80 2 91 Torino 81 93 93 19 102 53 Avellino 82 21 40 64 76 78 Catanzaro 83 28 4 92 74 88 Benevento 84 20 42 77 14 86 Terni 85 89 67 74 59 62 Reggio C. 86 1 23 78 84 99 Matera 87 43 53 87 7 75 Nuoro 88 12 55 103 19 80 Isernia 89 56 80 85 17 73 Napoli 90 38 5 7 80 103 Cosenza 91 29 54 93 22 90 Siracusa 92 6 32 82 73 96 Vibo V. 93 30 21 96 87 89 Trapani 94 17 27 91 54 92 Catania 95 5 22 52 83 100 Palermo 96 15 17 47 75 101 Campobasso 97 57 78 86 51 74 Agrigento 98 7 15 94 86 97 Potenza 99 76 63 99 21 76 Crotone 100 9 3 100 56 102 Foggia 101 22 64 90 26 93 Caltanissetta 102 8 76 95 85 94 Enna 103 36 72 102 97 85 Note: Source: Istat, Inps and Agenzia del Territorio.

20

6

Conclusions

This paper uses the hedonic approach to measure and compare quality of life across Italian cities on the basis of compensating differentials in housing and labor markets. We analyze micro-level data on house transactions from the Real Estate Observatory of the Agenzia del Territorio and on wages and job characteristics from the Italian National Social Security Institute, merged with city-level characteristics for the municipalities of the 103 Italian provinces for the period 2001-2008. We find that the presence of amenities results in large compensating differentials in the housing market. On the other hand, there is no clear evidence of compensating differentials in the labor market. This might reflect the productivity effects of amenities or, more plausibly, the relative rigidity of wages and low regional mobility in the Italian labor market. Local amenities account for substantial variation in quality of life. The bundle of amenities available in the cities with the highest quality of life command a premium, relative to the average, of about one third of the average annual salary for an Italian household. Indeed, a representative household would be willing to give up approximately 40 per cent of its average annual wage for not living in a city with the worst bundle of amenities. Overall, quality of life is highest in medium-sized towns of the Center-North. Cities located in the South generally display low quality of life, with 6 out of 10 of the last cities in the ranking being located in Sicily. Focusing on quality of life domains, cities in the North generally fare better with respect to services and socioeconomic conditions, while relatively worse for climatic and environmental conditions. The opposite pattern applies to cities located in the South. Cities in the Center-North are generally characterized by relatively high scores in all the domains considered. The domain-specific indicators are related to the overall index in various degrees. The climate and environment indices are negatively related to overall QoL, while the society index is positively but weakly related to QoL. Services and, to a larger extent, economic conditions are strongly related to overall QoL. Overall, our comparisons of quality of life across cities on the basis of revealed preferences provide objective information that is particularly relevant to inform the debate on fiscal federalism, while also indicating specific directions for economic, urban, and environmental policy. More generally, they highlight the importance for the municipal, regional and central governments of establishing information systems for monitoring the variables affecting urban quality of life. This would significantly improve our ability to detect disparities in quality of life across cities and to identify their main causes.

21

Data Appendix House prices are from the Real Estate Observatory of the ”Agenzia del Territorio” (AT), a public agency within the Ministry of the Economy. AT is responsible for classifying houses and land in the entire Italian territory. We have selected the data on individual house transactions in Italian cities (municipalities of province capitals) from 2004 to 2009. In addition to house transaction prices, the data set provides a detailed description of housing characteristics, such as floor surface area, number of bathrooms, floor level, number of garages or car parks, location (center, semi-center, suburb), quality of building (good, average, bad) quality of the area, distance from transport system. Labor market data are obtained from the Italian National Social Security Institute (Inps). We use the Employee’s archive, containing information on workers employed in the private sector who are insured with Inps. Wages refer to private sector workers’ annual earnings. In addition, the data set provides information on the type of occupation, whether the job is full time or part-time, contract length, province of work, sector of economic activity. Personal and demographic characteristics include gender, age, nationality, province of residence. Information on city characteristics and amenities have been collected from several sources, as detailed in Table 10. Climatic data are from Istat and other specific sources (www.ilmeteo.it). The variables refer to monthly temperature, monthly millimetres of precipitations and annual average humidity. Environmental variables are collected from Istat and include the share of green areas of the total city area and to the number of polluting agents in the air. A dummy variable identifies cities bordering with the sea (the dummy is coded 1 if the centre of the city is less than 10 kilometres from the coast). We measure services as education, transport and culture. For education we include a measure of the teacher/pupil ratio (average of primary and secondary schools), from Italian Ministry of Education): For transport we include an accessibility measure (multimodal measure that considers accessibility by air, train and car, index= 100 for the European average, source ESPON project (www.espon.eu). Finally, we measure cultural conditions with an index of the cultural infrastructure of the city (accounting for museums, theatres, cinemas, libraries, gyms). The index is set to 100 for the Italian average (source: Istituto Tagliacarne). The number of violent crime acts per capita is from the Ministry of Justice, while we use the voters’ turnout in local elections (Ministry of Interior) as a measure of the degree of participation of the society in public decisions. Finally we measure for the share of population enrolled in university (source Ministry of Education) and the share of foreigners in resident population (source Istat). We account for demographic factors by including a measure 22

of urban density and of population size. Economic conditions are measured by value added per capita and the unemployment rate. We also include among control variables an indicator of the economic structure (share of service sector). All variables are from Istat.

Table 10: Description and sources of variables Variable Description Precipitation Millimeters of rain per month, average over 12 months. Source: ilmeteo.it and Istat Temperature Average temperature over the year. Source: ilmeteo.it and Istat Humidity Air humidity, percentage, yearly average. Source: www.ilmeteo.it and Istat Coast Dummy equal to 1 if city within 10 kilometers from the coast. Source: authors’ calculation Green areas Percentage of urban green over urban area. Source: Istat Air Pollution Nunber of polluting agents in the air. Source: Istat Education Teacher/pupil ratio, per cent, average of primary, secondary and upper secondary schools. Source: Italian Ministry of Education Transport Multimodal (train, air, car) accessibility index, Espon space = 100. Source: Espon Culture Index of cultural infrastructure, Italian average = 100. Source: Istituto Tagliacarne Crime Number of violent crimes per 1000 inhabitants. Source: Istat. Civicness Voting turnout in administrative elections, per cent. Source: Italian Interior Ministry and Istat University Enrollment Per cent of resident population. Source: Istat and Italian Education Ministry. Foreigners Share of foreign residents. Source: Istat. Value Added Per head, thousand euros. Source: Istat. Unemployment Percentage rate. Source: Istat.

23

Table 11: Descriptive statistics, housing characteristics Variable Mean Std. Dev. Min. Max. Total surface (log) 4.61 0.39 2.56 6.21 Age of building 38.8 34.3 0 505 Floor 2.11 1.73 0 27 Number of floors 4.60 2.26 0 29 Number of bathrooms 3.35 2.7 1 7 Penthouse 0.01 0.12 0 1 Elevator 0.55 0.5 0 1 Housing type: flat 0.74 0.44 0 1 Housing type: economic 0.22 0.41 0 1 Housing type: luxury 0.01 0.09 0 1 Housing type: house 0.03 0.18 0 1 Value type: offer price 0.45 0.5 0 1 Value type: sale price 0.39 0.49 0 1 Value type: market price 0.16 0.37 0 1 Conditions: normal 0.87 0.34 0 1 Conditions: good 0.11 0.31 0 1 Conditions: poor 0.02 0.16 0 1 Features: exclusive area 0.08 0.26 0 1 Features: garage 0.16 0.37 0 1 Features: balcony 0.11 0.31 0 1 Features: attic 0.04 0.19 0 1 Features: basement 0.23 0.42 0 1 Location: central 0.24 0.43 0 1 Location: peripheric 0.31 0.46 0 1 Location: rural 0 0.03 0 1 Location: semi-central 0.28 0.45 0 1 Location: sub-urban 0.17 0.38 0 1 Location qual.: very poor 0.01 0.11 0 1 Location qual.: normal 0.9 0.31 0 1 Location qual.: very good 0.09 0.29 0 1 Location qual.: poor 0 0 0 0 Public transport: absent 0.01 0.12 0 1 Public transport: distant 0.1 0.3 0 1 Public transport: near 0.88 0.32 0 1 Services: absent 0.02 0.15 0 1 Services: distant 0.21 0.41 0 1 Services: near 0.76 0.43 0 1 Comm. services: absent 0.01 0.11 0 1 Comm. services: distant 0.13 0.34 0 1 Comm. services: near 0.85 0.35 0 1 24

N 134315 134369 131508 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369 134369

N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Table 12: Average real housing prices, 2004-2009, Province Val. N Province Val. N Siena 384028 36 Latina 200666 71 Salerno 369168 37 Torino 199239 72 Roma 363078 38 Vicenza 198772 73 Firenze 356853 39 Arezzo 198665 74 Milano 325199 40 Lecco 198513 75 Napoli 311194 41 Aosta 196649 76 Bologna 299648 42 Pescara 193765 77 Lucca 292125 43 Brescia 192055 78 Bolzano 290317 44 Piacenza 191146 79 Venezia 280966 45 Mantova 186851 80 Caserta 280165 46 Potenza 185259 81 Treviso 278096 47 Catania 183942 82 Trento 266586 48 Ravenna 182270 83 Prato 264490 49 Belluno 181136 84 Palermo 262354 50 Pistoia 180367 85 Pisa 248328 51 Frosinone 178320 86 Massa 244877 52 Perugia 177813 87 Rimini 238485 53 Udine 174520 88 Cagliari 238229 54 Trieste 171415 89 Pesaro 235704 55 Verbania 170906 90 Savona 235328 56 Campobasso 170183 91 Como 232349 57 Cuneo 168619 92 Bari 228996 58 Lodi 167080 93 Livorno 226524 59 Foggia 166678 94 Avellino 225124 60 La Spezia 166126 95 Parma 224648 61 Reggio E. 163582 96 Ancona 223659 62 Ascoli P. 163429 97 Bergamo 221429 63 Alessandria 163177 98 Padova 221151 64 Macerata 162612 99 Imperia 220803 65 Lecce 160142 100 Grosseto 220054 66 Forli 159983 101 Genova 208728 67 Benevento 159860 102 Modena 207094 68 Cosenza 159558 103 Verona 202475 69 Varese 158171 Pavia 200759 70 L’Aquila 154560

Note: Source: Agenzia del Territorio.

25

by city Province Isernia Agrigento Matera Crotone Sondrio Asti Teramo Viterbo Rieti Sassari Cremona Novara Pordenone Ferrara Catanzaro Nuoro Messina Reggio C. Taranto Rovigo Brindisi Enna Chieti Ragusa Biella Terni Siracusa Oristano Gorizia Vercelli Caltanissetta Vibo V. Trapani

Val. 153963 152373 151880 150062 149523 147828 145778 145587 144907 143912 142159 141863 138692 135895 135514 135224 132253 130105 129706 128351 128272 128057 125012 124624 122036 120924 120882 120205 116406 115559 109018 104547 99095

Table 13: Descriptive statistics, worker-firm characteristics Variable Mean Std. Dev. Min. Max. Male 0.66 0.47 0 1 Age 37.65 10.15 16 75 Age squared 1520.61 801.41 256 5625 Number of paid days 291.32 37.27 151 365 Nationality: Asia 0.01 0.09 0 1 Nationality: Africa 0.02 0.14 0 1 Nationality: Latin America 0 0.06 0 1 Executive 0 0.06 0 1 Manager and white collar 0.39 0.49 0 1 Blue collar 0.56 0.5 0 1 Apprentice 0.04 0.21 0 1 Temporary contract 0.06 0.23 0 1 Small firm 0.27 0.44 0 1 Medium firm 0.44 0.5 0 1 Large firm 0.26 0.44 0 1 Agriculture 0 0.06 0 1 Electricity 0.02 0.13 0 1 Chemistry 0.06 0.24 0 1 Metalworking 0.2 0.4 0 1 Food, textile, wood 0.17 0.37 0 1 Building materials 0.08 0.27 0 1 Commerce and services 0.19 0.39 0 1 Tranport and commun. 0.06 0.23 0 1 Credit, insurance 0.12 0.33 0 1 Public admin. 0.1 0.3 0 1

26

N 168255 168255 168255 168255 168255 168255 168255 168255 168255 168255 168255 168255 168257 168257 159983 159983 159983 159983 159983 159983 159983 159983 159983 159983 159983

N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

Table 14: Average real wages, 2001-2002, by Province Val. N Province Val. N Milano 21682 36 Cagliari 18104 71 Roma 21300 37 L’Aquila 18082 72 Torino 20263 38 Pordenone 18056 73 Bologna 20121 39 Vercelli 17931 74 Trieste 20014 40 Rieti 17886 75 Parma 20014 41 Vicenza 17833 76 Genova 19927 42 Napoli 17723 77 Bolzano 19771 43 Terni 17709 78 Modena 19623 44 Ferrara 17706 79 Reggio E. 19513 45 Alessandria 17694 80 Lecco 19295 46 Padova 17690 81 Pesaro 19259 47 Treviso 17671 82 Pistoia 19259 48 Palermo 17648 83 Prato 19179 49 Latina 17643 84 Piacenza 19092 50 Reggio C. 17636 85 Livorno 19008 51 Novara 17634 86 Firenze 18942 52 Gorizia 17629 87 Verona 18931 53 Pisa 17628 88 Como 18829 54 Campobasso 17570 89 Varese 18792 55 Siracusa 17525 90 Trento 18764 56 Rimini 17469 91 Brescia 18632 57 Lucca 17462 92 Siena 18601 58 Biella 17437 93 Aosta 18535 59 Asti 17426 94 Bergamo 18461 60 Massa 17371 95 Pavia 18458 61 Frosinone 17279 96 Cremona 18446 62 Sondrio 17243 97 La Spezia 18389 63 Catania 17210 98 Venezia 18355 64 Ancona 17148 99 Savona 18354 65 Messina 17089 100 Cuneo 18324 66 Foggia 17073 101 Ravenna 18234 67 Caltanissetta 17030 102 Udine 18163 68 Belluno 16935 103 Lodi 18158 69 Sassari 16891 Mantova 18148 70 Forli 16876

Note: Source: Inps.

27

city Province Viterbo Isernia Pescara Taranto Cosenza Brindisi Verbania Ascoli P. Chieti Grosseto Perugia Salerno Agrigento Imperia Arezzo Bari Macerata Potenza Trapani Rovigo Catanzaro Matera Oristano Teramo Caserta Avellino Nuoro Vibo V. Crotone Lecce Benevento Ragusa Enna

Val. 16811 16786 16769 16702 16668 16654 16571 16555 16522 16474 16460 16448 16415 16318 16310 16287 16237 16204 16097 16065 16052 15877 15770 15755 15752 15690 15461 15300 15262 15238 15196 15155 14940

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